Our last highlight featured Parti founder, Bryan, pushing boundaries by giving a voice to the dead. Come check out the latest highlight. It can be difficult for us to imagine the future of AI, as it is something that does not yet exist. However, we are starting to get glimpses of what it might look like. AI already has the power to entice anyone, and it is becoming clear that it will play a major role in our future - even in ways like this.

An image generator is a software that creates images from the prompt text that can be used many purposes such in Graphic designing, Book templates etc. Many companies will use Image generator for creating new designs for anything they want. Many people will also use image generators for more traditional purposes, such as creating memes and creating artworks. Image generators are very useful because they can create an unlimited number of images without the need to find models or real world images. Even sometimes images are purely fictions but it does look like it fictions. DALL-E 2: OpenAI DALL-E 2 is an AI model developed by OpenAI that has been trained to generate images from text .

A green sign that says "Very Deep Learning" and is at the edge of the Grand Canyon. Puffy white clouds are in the sky. There is a river in front of them with water lilies.

Polarization and echo chambers are often studied in the context of explicitly political events such as elections, and little scholarship has examined the mixing of political groups in non-political contexts. A major obstacle to studying political polarization in non-political contexts is that political leaning (i.e., left vs right orientation) is often unknown. Nonetheless, political leaning is known to correlate (sometimes quite strongly) with many lifestyle choices leading to stereotypes such as the "latte-drinking liberal." We develop a machine learning classifier to infer political leaning from non-political text and, optionally, the accounts a user follows on social media. We use Voter Advice Application results shared on Twitter as our groundtruth and train and test our classifier on a Twitter dataset comprising the 3,200 most recent tweets of each user after removing any tweets with political text. We correctly classify the political leaning of most users (F1 scores range from 0.70 to 0.85 depending on coverage). We find no relationship between the level of political activity and our classification results. We apply our classifier to a case study of news sharing in the UK and discover that, in general, the sharing of political news exhibits a distinctive left-right divide while sports news does not.

The on tin uous hidden v ariables denote the temp o. Ex-a t omputation of p osterior features su h as the MAP state is in tra table in this mo del lass, so w e in tro du e Mon te Carlo metho ds for in tegration and optimization. The metho ds an b e applied in b oth online and bat h s enarios su h as temp o tra king and trans ription and are th us p oten tially useful in a n um b er of m usi appli ations su h as adaptiv e automati a ompanimen t, s ore t yp esetting and m usi information retriev al. 1. Ho w ev er, when op erating on sampled audio data from p olyphoni a ousti al signals, extra tion of a s ore-lik e des ription is a v ery hallenging auditory s ene analysis task (V er o e, Gardner, & S heirer, 1998). In this pap er, w e fo us on a subproblem in m usi -ir, where w e assume that exa t timing information of notes is a v ailable, for example as a stream of MIDI 1 ev en ts from a digital k eyb oard. One example is automati s ore t yp esetting, 1. Musi al Instrumen ts Digital In terfa e. Ea h time a k ey is pressed, a MIDI k eyb oard generates a short message on taining pit h and k ey v elo it y . In on v en tional m usi notation, the onset time of ea h note is impli itly represen ted b y the um ulativ e sum of durations of previous notes. Durations are en o ded b y simple rational n um b ers (e.g., quarter note, eigh th note), onsequen tly all ev en ts in m usi are pla ed on a dis rete grid. This is due to the fa t that m usi ians in tro du e in ten tional (and unin ten tional) deviations from a me hani al pres ription. F or example timing of ev en ts an b e delib erately dela y ed or pushed. Moreo v er, the temp o an u tuate b y slo wing do wn or a elerating. In fa t, su h deviations are natural asp e ts of expressiv e p erforman e; in the absen e of these, m usi tends to sound rather dull and me hani al. On the other hand, if these deviations are not a oun ted for during trans ription, resulting s ores ha v e often v ery p o or qualit y . Robust and fast quan tization and temp o tra king is also an imp ortan t requiremen t for in tera tiv e p erforman e systems; appli ations that \listen" to a p erformer for generating an a ompanimen t or impro visation in real time (Raphael, 2001b; Thom, 2000). A t last, su h mo dels are also useful in m usi ology for systemati study and hara terization of expressiv e timing b y prin ipled analysis of existing p erforman e data. F rom a theoreti al p ersp e tiv e, sim ultaneous quan tization and temp o tra king is a \ hi k en-and-egg" problem: the quan tization dep ends up on the in tended temp o in terpre-tation and the temp o in terpretation dep ends up on the quan tization. Apparen tly, h uman listeners an resolv e this am biguit y (in most ases) without an y eort.